Exploring Global Climate Model Downscaling Based on Tile-Level Output

Abstract

Statistical and dynamical modeling techniques are used to downscale global climate model (GCM) outputs to practical resolutions for local-or regional-scale applications. Current techniques do not incorporate the effects of land-use and land-cover changes, although research has shown that such changes can substantially affect climate locally. Here, we explore a new downscaling technique that uses tile-level GCM outputs provided under phase 6 of the Coupled Model Intercomparison Project (CMIP6). The method, land-cover tile downscaling (LTD), spatially locates the tile-level GCM outputs by mapping them to corresponding classes in high-resolution land-cover maps. Furthermore, it applies an elevation-based correction to account for the effect of topography on the local climate. LTD is applied to near-surface temperature outputs from the Community Earth System Model, version 2 (CESM2) and U.K. Earth System Model, version 1 (UKESM1), and surface temperature output from CESM2 and evaluated against observations. In comparison with grid-averaged control data, LTD outputs show an overall bias reduction that is not spatially consistent. Moreover, LTD performs better on air temperature data than on surface temperature and better on areas dominated by primary/secondary land and crops than on urban land. This could arise from simplifications in methods, like land-cover reclassification and simplified lapse rate esti-mates. However, the difference in response between the two variables and land-cover types implies that biases also stem from model structural features involved in estimating their tile-level outputs. This is supported by the differences between grid average data provided by the models and the same data reconstructed from tile-level outputs. Therefore, a thorough evaluation and quality control of tile-level outputs is recommended.